https://github.com/imoonlab/lighthgnn
Source code for ICLR 2024 "LightHGNN: Distilling Hypergraph Neural Networks into MLPs for 100x Faster Inference"
https://github.com/imoonlab/lighthgnn
Last synced: 8 months ago
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Source code for ICLR 2024 "LightHGNN: Distilling Hypergraph Neural Networks into MLPs for 100x Faster Inference"
- Host: GitHub
- URL: https://github.com/imoonlab/lighthgnn
- Owner: iMoonLab
- License: apache-2.0
- Created: 2024-01-24T06:38:42.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-01-31T05:45:56.000Z (about 2 years ago)
- Last Synced: 2025-04-07T12:52:01.511Z (12 months ago)
- Language: Python
- Size: 849 KB
- Stars: 30
- Watchers: 3
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# LightHGNN
This repository contains the source code for the paper "LightHGNN: Distilling Hypergraph Neural Networks into MLPs for 100x Faster Inference" published in IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2024 by [Yifan Feng](https://fengyifan.site/), Yihe Luo, Shihui Ying, Yue Gao*. This paper is available at [here](https://openreview.net/forum?id=lHasEfGsXL).
## Introduction
In this repository, we provide the implementation of our LightHGNNs, including LightHGNN and LightHGNN+, which is based on the following environments:
* [python 3.9](https://www.python.org/): basic programming language.
* [dhg 0.9.4](https://github.com/iMoonLab/DeepHypergraph): for hypergraph representation and learning.
* [torch 1.12.1](https://pytorch.org/): for computation.
* [hydra-core 1.3.2](https://hydra.cc/docs/intro/): for configuration and multi-run management.
## Installation
1. Clone this repository.
2. Install the required libraries.
``` bash
pip install -r requirements.txt
```
## Usage
For transtive setting, you can run the following command:
```bash
python trans_train.py
```
For multi-run and obtain the average results, you can run the following command:
```bash
python trans_multi_exp.py
```
For production setting, you can run the following command:
```bash
python prod_train.py
```
For multi-run and obtain the average results, you can run the following command:
```bash
python prod_multi_exp.py
```
**Change Models**
You can change the teacher by modifying the `teacher` in `trans_config.yaml` and `prod_config.yaml` as following:
```yaml
model:
teacher: hgnn # hgnn, hgnnp, hnhn, unigcn
```
Also, you can change the student by modifying the `student` in `trans_config.yaml` and `prod_config.yaml` as following:
```yaml
model:
student: light_hgnn # light_hgnn, light_hgnnp
```
**Change Datasets**
In our paper, 13 grpah/hypergraph datasets are adopted for evaluation.
- Graph datasets: `cora`, `pubmed`, `citeseer`
- Hypergraph datasets: `news20`, `ca_cora`, `cc_cora`, `cc_citeseer`, `dblp4k_conf`, `dblp4k_paper`, `dblp4k_term`, `imdb_aw`, `recipe_100k`, `recipe_200k`
You can change the dataset by modifying the `dataset` in `trans_config.yaml` and `prod_config.yaml` as following:
```yaml
data:
name: dblp4k_paper # cora, pubmed, news20, ca_cora, dblp4k_term, imdb_aw, ...
```
**Important Note**
Since the `recipe_100k` and `recipe_200k` datasets are too large and contains more than 10k vertices, the two dataset can only be used under the production setting. **Please do not use the two datasets for the transitive setting.**
## Citation
If you find this repository useful in your research, please cite the following papers:
```
@inproceedings{feng2024lighthgnn,
title={Light{HGNN}: Distilling Hypergraph Neural Networks into {MLP}s for 100x Faster Inference},
author={Feng, Yifan and Luo, Yihe and Ying, Shihui and Gao, Yue},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
}
@article{gao2022hgnn+,
title={HGNN+: General hypergraph neural networks},
author={Gao, Yue and Feng, Yifan and Ji, Shuyi and Ji, Rongrong},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume={45},
number={3},
pages={3181--3199},
year={2022},
publisher={IEEE}
}
@inproceedings{feng2019hypergraph,
title={Hypergraph neural networks},
author={Feng, Yifan and You, Haoxuan and Zhang, Zizhao and Ji, Rongrong and Gao, Yue},
booktitle={Proceedings of the AAAI conference on artificial intelligence},
volume={33},
number={01},
pages={3558--3565},
year={2019}
}
```